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dc.contributor.authorAl-Doori, Moathen
dc.contributor.authorPaluszczyszyn, D.en
dc.contributor.authorElizondo, Daviden
dc.contributor.authorPassow, Benjamin N.en
dc.contributor.authorGoodyer, E. N.en
dc.identifier.citationAl-Doori, M. et al. (2014) Range extended for electric vehicle based on driver behaviour. Hybrid and Electric Vehicles Conference (HEVC 2014), 5th IETen
dc.description.abstractDriver behaviour has been considered one of the main factors that contribute to increase fuel consumption, CO2 emissions, traffic accidents and causalities. Thus, the concept of detecting and classifying driver behaviour i s vital when tackling these challenges. Recognition of the driver behaviour is a difficult task as in the real-world, the driving behaviour is effected by many factors e.g. traffic, road conditions, duration of the journey etc. Many approaches have considered the use of Computational Intelligence techniques, to develop a driver behaviour detection system. In this paper we concentrate on the impact of driver behaviour on the energy consumption and thereby on the range of electric vehicles. A new architecture is proposed to show how computational intelligence techniques could interact with the contextual information collected from the vehicle, the driver and external environment. A neural network model is used to classify the driver behaviour, and then this classification is used in a fuzzy logic controller to make balanced managements to the range extender operation.en
dc.subjectfuzzy logicen
dc.subjectdriver behavioren
dc.subjectartificial neural networksen
dc.titleRange extended for electric vehicle based on driver behaviouren
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en

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